Re: Knowledge representation for AI using domains or a universe of discourse

While much of this exchange is beyond my level of comprehension and interest, Charles' inquiry prompted me to engage ChatGPT further on his point, to this conclusion:

✅ The "AI handshake" is more than an entry point—it’s an enabler of purpose-driven connections.✅ StratML provides the semantic fuel for AI to make those connections actionable.✅ The next steps should focus on automating discovery, proposing collaborations, and assembling the processes and resources needed to achieve objectives.

ChatGPT's logic in support of its conclusion is available at:https://chatgpt.com/share/685ebfef-10fc-800b-b464-b5138211eacc
If anyone is interested, it offers to "draft a StratML plan specifically for the AI Handshake Service concept to crystallize this as a formal initiative."
Owen Amburhttps://www.linkedin.com/in/owenambur/
 

    On Saturday, June 28, 2025 at 04:56:33 AM EDT, Paola Di Maio <paoladimaio10@gmail.com> wrote:   

 Charles,
I am glad you *and other newcomers are taking an interest in AI KR!i  have un cc d the other groups btw as KR may be not relevant to them
I hope you can stick around and help us learn what we may not know
A good starting point
Two ontologists walk into a bar.

One says, “Is this bar universally instantiated or just a particular instance?”The other replies, “I don’t care, as long as the drinks have essential properties.”
it is a very dense topic that needs to be studied formally. It has developed over the course of fifty years, it is evolving fastSo fast, that even experts lose sight of the leading edge 
The first thing newcomers can do - to help themselves and everyone else -  is to read the resources, *start with the group's wiki.  take one or more of the freely available courses, the wikipedia entry, etcread the books, check the discussions on this list archive, and familiarize with the task at hand*you ll figure out what we are doing when you read the posts on the mailing list
Please note that we are not developing a standard for pharmaceutical or any other domaiin knowledge here, at allL-)  I ll leave it up to you to figure out what we are doing, and contribute according to your interest and understanding of the topicafter you have a look. it is going to make a good entertainment for the summer
I share below a snippet of a resource  which I hope can serve as orientation for newcomers and anyone else who has been around a while and still finds AI KR hard to grasp. You are in good company. 
I ll  try to be off for the summary as I have other deadlines, but will be back!




On Sat, Jun 28, 2025 at 4:30 PM CHARLES WAWERU <cmukabi@aol.com> wrote:

Let me ask as I am new to this . If today you were to meet me in person what is the first thing you will do/ask? I bet it will be ‘how are you?’ or ‘Greetings Charles ‘ . That initial interaction during a meet up , what is its equivalent in KR when AI seeking information ? Does it interrogate knowledge/data? Many thanks Charles 
Sent from my iPhone
http://share.payoneer.com/nav/QVCAMdwwLEzTHFv0LgtPy4MhJQ3AGwSli1rtz8A95x9tBafV8puBAroUEusyMVSg32hIAvEKvhibLKoacJc_Og2


On 28 Jun 2025, at 02:14, Milton Ponson <rwiciamsd@gmail.com> wrote:



OK, here is a more precise description of what I am looking for in knowledge representation. 
First let's look at the Wikipedia page:https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
My dealing with knowledge in the vast domain of sustainable development which interacts with all academic fields, has convinced me that knowledge exists in context.
And then there is the resolution of knowledge, or more succinctly put granularity of knowledge.
The Buddhist philosophy of Madhyamaka, The Middle Way utilizes dependent origination to show the interdependence of phenomena. 
When we Google "dependent origination and knowledge granularity" the resulting AI Overview is really enlightening. 
And when we Google "granularity of knowledge" in the search result the following article stands out:
 I quote the following:Abstract:Granularity of knowledge has attracted attention of many researchers. This paper concerns this issue from the rough set perspective. Granularity is inherently connected with the foundation of rough set theory. The concept of the rough set hinges on classification of objects of interest into similarity classes, which form elementary building blocks (atoms, granules) of knowledge. These granules are employed to define basic concepts of the theory. In the paper basic concepts of rough set theory are defined and their granular structure pointed out. Next the consequences of granularity of knowledge for reasoning about imprecise concepts are discussed.
Jumping back to the Wikipedia page I want to highlight the article:A Theory of Formalisms for Representing Knowledge,https://ojs.aaai.org/index.php/AAAI/article/view/33674
It should now become apparent why both Paola and I are in essence talking about the same thing, but since artificial intelligence is used to act upon input, react to stimuli or solve problems etc. what we are implicitly trying to build is knowledge representation and reasoning for AI.
Using rough set theory, category theory and other fields of mathematics that define frames, frameworks, structures, templates, types or models we find that my reasoning for classification of objects of interest in similarity classes is well founded.And the Wikipedia page summarizes IMHO pretty much everything that is relevant for KR for AI.
Milton PonsonRainbow Warriors Core FoundationCIAMSD Institute-ICT4D Program+2977459312PO Box 1154, OranjestadAruba, Dutch Caribbean

On Fri, Jun 27, 2025, 09:23 Paola Di Maio <paoladimaio10@gmail.com> wrote:

Milton, Great to see you engage with the topic and with the categorization of knowledge
I am reminded that several knowledge categorization systems exist 
It would be relevant to us to understand how the traditional knowledge categories that you citerooted in epistemoligy, relate to the knowledge representation for AI *which is the focus of this CG
The knowledge domain for AI KR is not the domain of 'knowledge categorization' it would be nonetheless be very relevant to KR, perhaps to explore and make explicit the relations between the two
PDM

On Thu, Jun 26, 2025 at 8:49 PM Milton Ponson <rwiciamsd@gmail.com> wrote:

Excellent. And ChatGPT made recommendations about expanding, tweaking and refining domains in line with my review of my initial list. I think we can make additional refinements by adding a not exhaustive list of directives,  guidelines, legislation ( in casu EU, the Eu AI Act) currently existing in countries worldwide for ethical, open, accountable,  trustworthy etc AI use.
Milton PonsonRainbow Warriors Core FoundationCIAMSD Institute-ICT4D Program+2977459312PO Box 1154, OranjestadAruba, Dutch Caribbean

On Wed, Jun 25, 2025, 23:10 Owen Ambur <owen.ambur@verizon.net> wrote:

Milton, while I can imagine there might be quite a lot of quibbling over the domains, this additional information is now included at https://stratml.us/docs/EARP.xml#uuid-1f8a0ae6-1b0a-4d1b-a201-5c8c9d1c8c01
ChatGPT suggests some potential modifications.
Owen Amburhttps://www.linkedin.com/in/owenambur/
 

    On Wednesday, June 25, 2025 at 12:48:02 PM EDT, Milton Ponson <rwiciamsd@gmail.com> wrote:   

 Thanks Owen,
I sense you were on to what I intend to do. Because the United Nations has the UNESCO, WIPO, ISO, and UNSD (UN Statistics Division) which together deal with various aspects of data and information, standardization and intellectual property rights issues, for scientific knowledge the process is more or less straightforward. 
In the UN universe of discourse, knowledge is is a concept spanning many domains of discourse. Traditional, indigenous and orally transmitted knowledge is the first category.It is currently being dealt with through SIL (https://www.sil.org),   UNESCO and several initiatives by organizations of indigenous peoples dealing with AI, traditional knowledge, data and knowledge sovereignty issues.
All the other categories of knowledge and data can be categorized either by universal subject coding sysyems, structured text components, or enumeration of informal text parameters. Semiotics, symbol sets, pictograms, petroglyphs and alphabets, spectral data are general categories which help define knowledge objects.
What needs to be done is to create a universal categorization for knowledge over the following domains of discourse:   
   - Mathematics;
   - Computer Science;
   - Physics and Astronomy;
   - Chemistry, Biology and Biochemistry;
   - Medical, Health and Life Sciences, including Genomics and Pharmacy;
   - Earth Sciences; dealing with lithosphere, hydrosphere and atmosphere
   - Marine Science including Oceanography;
   - Engineering and Materials Sciences;
   - Languages and Computational Linguistics;
   - Philosophy;
   - Social Sciences, including History, Political Sciences, Sociology and Anthropology;
   - Art, Cultural and Creative Design Studies;
   - Education, Vocational and Professional Training;
   - Economics, Econometrics and Statistics;
   - Political Sciences, and Law;
   - Business and Public Sector Management.
Milton PonsonRainbow Warriors Core FoundationCIAMSD Institute-ICT4D Program+2977459312PO Box 1154, OranjestadAruba, Dutch Caribbean

On Wed, Jun 25, 2025, 11:54 Owen Ambur <owen.ambur@verizon.net> wrote:

To help me make sense of this, I prompted ChatGPT to render a plan in StratML Part 2 format, which is now available at https://stratml.us/drybridge/index.htm#EARP or, more specifically, https://stratml.us/docs/EARP.xml
Owen Amburhttps://www.linkedin.com/in/owenambur/
 

    On Tuesday, June 24, 2025 at 10:48:45 PM EDT, Paola Di Maio <paola.dimaio@gmail.com> wrote:   

 Thank you MiltonLet us know how you propose to advance this suggestionPDM
On Wed, Jun 25, 2025 at 6:27 AM Milton Ponson <rwiciamsd@gmail.com> wrote:

Dear all,
We have reached the point in time where we must start to generalize the basis of knowledge representation for artificial intelligence.
In order to do so we must find (a) common denominator(s) for the different types of knowledge.
Assuming that the knowledge is digitized we can use the concept of the universe of discourse or more specifically domains of discourse.
These concepts lend themselves for use of set theory, type theory etc. and allow modeling and constructibility of consistent logical frameworks.
And they also allow the use of (hyper)graphs, algebraic geometry, category theory etc. to allow knowledge representation in ways that can be captured for data (structure) creation, data analysis and machine learning.
In addition we can use classification systems to uniquely identify domains of discourse as librarians do and as mathematicians do as well with the Mathematics Subject Classification. 
This can lead to a much more efficient use of datasets, the use of which can be negotiated with the respective owners, avoiding the current intellectual property and data rights, data and knowledge sovereignty debates.
The indiscriminate use of scraping and unlimited production of internet content by artificial intelligence is contaminating available data.
After having examined the current use of artificial intelligence for solving sustainable development problems, both at global, regional and local levels, the use of (open) curated and/or standardized datasets is the only way to go.
This requires the use of a universe of discourse and more specifically domains of discourse.

Milton PonsonRainbow Warriors Core FoundationCIAMSD Institute-ICT4D Program+2977459312PO Box 1154, OranjestadAruba, Dutch Caribbean